曙海教學優勢
本課程，秉承二十一年積累的教學品質，以項目實現為導向,面向企事業項目實際需要,老師將會與您分享設計的全流程以及工具的綜合使用經驗、技巧。課程可定制,線上/線下/上門皆可,熱線:4008699035。
曙海培訓的課程培養了大批受企業歡迎的工程師。大批企業和曙海
建立了良好的合作關系,合作企業30萬+。曙海培訓的課程在業內有著響亮的知名度。
?此課程重點介紹 MATLAB 中使用 Statistics Toolbox , Machine Learning Toolbox? 和
Deep Learning Toolbox? 功能的數據分析和機器學習技術。本課程
演示如何通過非監督學習發現大數據集的特點，以及通過監督學
習建立預測模型。課程中的示例和練習強調用于呈現和評估結果
的技巧。內容包括：
|
Importing and Organizing Data |
Objective:?Bring data into MATLAB and organize it for analysis, including normalizing data and removing observations with missing values. ·?Data types ·?Tables ·?Categorical data ·?Data preparation |
|
Finding Natural Patterns in Data |
Objective:?Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set. ·?Unsupervised learning ·?Clustering methods ·?Cluster evaluation and interpretation |
|
Building Classification Models |
Objective:?Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model. ·?Supervised learning ·?Training and validation ·?Classification methods |
|
Improving Predictive Models |
Objective:?Reduce the dimensionality of a data set. Improve and simplify machine learning models. ·?Cross validation ·?Hyperparameter optimization ·?Feature transformation ·?Feature selection ·?Ensemble learning |
|
Building Regression Models |
Objective:?Use supervised learning techniques to perform predictive modeling for continuous response variables. ·?Parametric regression methods ·?Nonparametric regression methods ·?Evaluation of regression models |
|
Creating Neural Networks |
Objective:?Create and train neural networks for clustering and predictive modeling. Adjust network architecture to improve performance. ·?Clustering with Self-Organizing Maps ·?Classification with feed-forward networks ·?Regression with feed-forward networks |
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